GCP_analysis.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": true
   },
   "source": [
    "### Evaluate distribution of ground control points\n",
    "\n",
    "Working toward some metrics to quantify the GCP distribution. This calculates the distribution of GCP distances \n",
    "and the number of GCPs / m^2, based on the convex hull area.\n",
    "\n",
    "TODO:\n",
    "* Maybe dilate the convex hull area to represent the additional area covered by targets on the perimeter (shapely?)\n",
    "* Normalize the area by something to take out camera affects (camera footprint? GSD^2? Altidude^2?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\\2017-04-28_GCPs.txt\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Label</th>\n",
       "      <th>Enable</th>\n",
       "      <th>X/Easting</th>\n",
       "      <th>Y/Northing</th>\n",
       "      <th>Z/Altitude</th>\n",
       "      <th>Accuracy_X/Y/Z_(m)</th>\n",
       "      <th>Error_(m)</th>\n",
       "      <th>X_error</th>\n",
       "      <th>Y_error</th>\n",
       "      <th>Z_error</th>\n",
       "      <th>X_est</th>\n",
       "      <th>Y_est</th>\n",
       "      <th>Z_est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T18</td>\n",
       "      <td>1</td>\n",
       "      <td>377201.5432</td>\n",
       "      <td>4.624613e+06</td>\n",
       "      <td>1.6758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022388</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.020059</td>\n",
       "      <td>377201.544236</td>\n",
       "      <td>4.624613e+06</td>\n",
       "      <td>1.695859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T20</td>\n",
       "      <td>1</td>\n",
       "      <td>377034.3030</td>\n",
       "      <td>4.624693e+06</td>\n",
       "      <td>1.7620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.046752</td>\n",
       "      <td>-0.041852</td>\n",
       "      <td>-0.005634</td>\n",
       "      <td>0.020059</td>\n",
       "      <td>377034.261157</td>\n",
       "      <td>4.624693e+06</td>\n",
       "      <td>1.782059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T19</td>\n",
       "      <td>1</td>\n",
       "      <td>377151.6261</td>\n",
       "      <td>4.624637e+06</td>\n",
       "      <td>1.1600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071421</td>\n",
       "      <td>-0.066701</td>\n",
       "      <td>-0.017925</td>\n",
       "      <td>-0.018184</td>\n",
       "      <td>377151.559414</td>\n",
       "      <td>4.624637e+06</td>\n",
       "      <td>1.141816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T17</td>\n",
       "      <td>1</td>\n",
       "      <td>377331.4144</td>\n",
       "      <td>4.624573e+06</td>\n",
       "      <td>-0.4845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040367</td>\n",
       "      <td>0.034440</td>\n",
       "      <td>0.013799</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>377331.448833</td>\n",
       "      <td>4.624573e+06</td>\n",
       "      <td>-0.500406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T16</td>\n",
       "      <td>1</td>\n",
       "      <td>377225.2911</td>\n",
       "      <td>4.624656e+06</td>\n",
       "      <td>2.5041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040200</td>\n",
       "      <td>0.010731</td>\n",
       "      <td>0.037140</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>377225.301829</td>\n",
       "      <td>4.624656e+06</td>\n",
       "      <td>2.515124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>T21</td>\n",
       "      <td>1</td>\n",
       "      <td>376941.3262</td>\n",
       "      <td>4.624769e+06</td>\n",
       "      <td>1.0644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012309</td>\n",
       "      <td>-0.002427</td>\n",
       "      <td>-0.008653</td>\n",
       "      <td>-0.008410</td>\n",
       "      <td>376941.323773</td>\n",
       "      <td>4.624769e+06</td>\n",
       "      <td>1.055990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T8</td>\n",
       "      <td>1</td>\n",
       "      <td>376539.5396</td>\n",
       "      <td>4.624997e+06</td>\n",
       "      <td>1.4488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019616</td>\n",
       "      <td>-0.019105</td>\n",
       "      <td>0.004137</td>\n",
       "      <td>-0.001640</td>\n",
       "      <td>376539.520499</td>\n",
       "      <td>4.624997e+06</td>\n",
       "      <td>1.447160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T14</td>\n",
       "      <td>1</td>\n",
       "      <td>377075.8505</td>\n",
       "      <td>4.624755e+06</td>\n",
       "      <td>2.3949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016997</td>\n",
       "      <td>0.006149</td>\n",
       "      <td>-0.014622</td>\n",
       "      <td>-0.006106</td>\n",
       "      <td>377075.856648</td>\n",
       "      <td>4.624755e+06</td>\n",
       "      <td>2.388794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P3</td>\n",
       "      <td>1</td>\n",
       "      <td>376537.6211</td>\n",
       "      <td>4.625135e+06</td>\n",
       "      <td>5.2726</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048220</td>\n",
       "      <td>-0.020931</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>0.040082</td>\n",
       "      <td>376537.600173</td>\n",
       "      <td>4.625135e+06</td>\n",
       "      <td>5.312682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>T7</td>\n",
       "      <td>1</td>\n",
       "      <td>376507.8345</td>\n",
       "      <td>4.625230e+06</td>\n",
       "      <td>-0.2558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024304</td>\n",
       "      <td>0.010554</td>\n",
       "      <td>-0.020032</td>\n",
       "      <td>-0.008833</td>\n",
       "      <td>376507.845052</td>\n",
       "      <td>4.625230e+06</td>\n",
       "      <td>-0.264633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>T10</td>\n",
       "      <td>1</td>\n",
       "      <td>377002.4223</td>\n",
       "      <td>4.624840e+06</td>\n",
       "      <td>1.1417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023834</td>\n",
       "      <td>0.021347</td>\n",
       "      <td>-0.010581</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>377002.443643</td>\n",
       "      <td>4.624840e+06</td>\n",
       "      <td>1.142332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T1</td>\n",
       "      <td>1</td>\n",
       "      <td>376822.5851</td>\n",
       "      <td>4.624961e+06</td>\n",
       "      <td>0.4622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050853</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>-0.049194</td>\n",
       "      <td>-0.012776</td>\n",
       "      <td>376822.586740</td>\n",
       "      <td>4.624961e+06</td>\n",
       "      <td>0.449424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>P2</td>\n",
       "      <td>1</td>\n",
       "      <td>376701.5234</td>\n",
       "      <td>4.624987e+06</td>\n",
       "      <td>5.7394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056585</td>\n",
       "      <td>0.015368</td>\n",
       "      <td>0.006360</td>\n",
       "      <td>0.054086</td>\n",
       "      <td>376701.538765</td>\n",
       "      <td>4.624987e+06</td>\n",
       "      <td>5.793486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>T4</td>\n",
       "      <td>1</td>\n",
       "      <td>376574.8225</td>\n",
       "      <td>4.625128e+06</td>\n",
       "      <td>1.3166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010903</td>\n",
       "      <td>-0.005146</td>\n",
       "      <td>-0.007873</td>\n",
       "      <td>-0.005514</td>\n",
       "      <td>376574.817356</td>\n",
       "      <td>4.625128e+06</td>\n",
       "      <td>1.311086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>P4</td>\n",
       "      <td>1</td>\n",
       "      <td>376418.4583</td>\n",
       "      <td>4.625330e+06</td>\n",
       "      <td>-0.3006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018534</td>\n",
       "      <td>0.009101</td>\n",
       "      <td>0.013263</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>376418.467399</td>\n",
       "      <td>4.625330e+06</td>\n",
       "      <td>-0.291393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>T11</td>\n",
       "      <td>1</td>\n",
       "      <td>376891.2441</td>\n",
       "      <td>4.624911e+06</td>\n",
       "      <td>0.4995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036790</td>\n",
       "      <td>0.036330</td>\n",
       "      <td>0.005592</td>\n",
       "      <td>-0.001533</td>\n",
       "      <td>376891.280422</td>\n",
       "      <td>4.624911e+06</td>\n",
       "      <td>0.497967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>T3</td>\n",
       "      <td>1</td>\n",
       "      <td>376600.8723</td>\n",
       "      <td>4.625090e+06</td>\n",
       "      <td>2.1740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.016370</td>\n",
       "      <td>-0.002929</td>\n",
       "      <td>-0.006991</td>\n",
       "      <td>376600.888666</td>\n",
       "      <td>4.625090e+06</td>\n",
       "      <td>2.167009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P5</td>\n",
       "      <td>1</td>\n",
       "      <td>376541.5990</td>\n",
       "      <td>4.625084e+06</td>\n",
       "      <td>2.6216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>-0.015205</td>\n",
       "      <td>0.019061</td>\n",
       "      <td>-0.024996</td>\n",
       "      <td>376541.583798</td>\n",
       "      <td>4.625084e+06</td>\n",
       "      <td>2.596604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>T2</td>\n",
       "      <td>1</td>\n",
       "      <td>376725.5433</td>\n",
       "      <td>4.625038e+06</td>\n",
       "      <td>-0.5633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053930</td>\n",
       "      <td>-0.021455</td>\n",
       "      <td>-0.034266</td>\n",
       "      <td>-0.035692</td>\n",
       "      <td>376725.521850</td>\n",
       "      <td>4.625038e+06</td>\n",
       "      <td>-0.598992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>T6</td>\n",
       "      <td>1</td>\n",
       "      <td>376423.7044</td>\n",
       "      <td>4.625243e+06</td>\n",
       "      <td>1.7465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019515</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.018572</td>\n",
       "      <td>-0.005989</td>\n",
       "      <td>376423.704551</td>\n",
       "      <td>4.625243e+06</td>\n",
       "      <td>1.740511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>T5</td>\n",
       "      <td>1</td>\n",
       "      <td>376581.2840</td>\n",
       "      <td>4.625168e+06</td>\n",
       "      <td>-0.2327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023835</td>\n",
       "      <td>-0.013324</td>\n",
       "      <td>-0.019756</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>376581.270679</td>\n",
       "      <td>4.625168e+06</td>\n",
       "      <td>-0.232170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>T12</td>\n",
       "      <td>1</td>\n",
       "      <td>376918.4131</td>\n",
       "      <td>4.624828e+06</td>\n",
       "      <td>2.8079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015712</td>\n",
       "      <td>0.006892</td>\n",
       "      <td>0.014087</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>376918.419991</td>\n",
       "      <td>4.624828e+06</td>\n",
       "      <td>2.806940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>P6</td>\n",
       "      <td>1</td>\n",
       "      <td>376687.0821</td>\n",
       "      <td>4.624924e+06</td>\n",
       "      <td>2.6679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>0.004894</td>\n",
       "      <td>-0.014150</td>\n",
       "      <td>376687.080450</td>\n",
       "      <td>4.624924e+06</td>\n",
       "      <td>2.653750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>P1</td>\n",
       "      <td>1</td>\n",
       "      <td>376822.8060</td>\n",
       "      <td>4.624907e+06</td>\n",
       "      <td>5.3093</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053947</td>\n",
       "      <td>0.038573</td>\n",
       "      <td>0.016811</td>\n",
       "      <td>0.033761</td>\n",
       "      <td>376822.844565</td>\n",
       "      <td>4.624907e+06</td>\n",
       "      <td>5.343061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>T9</td>\n",
       "      <td>1</td>\n",
       "      <td>376615.5168</td>\n",
       "      <td>4.624785e+06</td>\n",
       "      <td>1.2540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>376615.516800</td>\n",
       "      <td>4.624785e+06</td>\n",
       "      <td>1.254000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Label  Enable    X/Easting    Y/Northing  Z/Altitude  Accuracy_X/Y/Z_(m)  \\\n",
       "0     T18       1  377201.5432  4.624613e+06      1.6758                 NaN   \n",
       "1     T20       1  377034.3030  4.624693e+06      1.7620                 NaN   \n",
       "2     T19       1  377151.6261  4.624637e+06      1.1600                 NaN   \n",
       "3     T17       1  377331.4144  4.624573e+06     -0.4845                 NaN   \n",
       "4     T16       1  377225.2911  4.624656e+06      2.5041                 NaN   \n",
       "5     T21       1  376941.3262  4.624769e+06      1.0644                 NaN   \n",
       "6      T8       1  376539.5396  4.624997e+06      1.4488                 NaN   \n",
       "7     T14       1  377075.8505  4.624755e+06      2.3949                 NaN   \n",
       "8      P3       1  376537.6211  4.625135e+06      5.2726                 NaN   \n",
       "9      T7       1  376507.8345  4.625230e+06     -0.2558                 NaN   \n",
       "10    T10       1  377002.4223  4.624840e+06      1.1417                 NaN   \n",
       "11     T1       1  376822.5851  4.624961e+06      0.4622                 NaN   \n",
       "12     P2       1  376701.5234  4.624987e+06      5.7394                 NaN   \n",
       "13     T4       1  376574.8225  4.625128e+06      1.3166                 NaN   \n",
       "14     P4       1  376418.4583  4.625330e+06     -0.3006                 NaN   \n",
       "15    T11       1  376891.2441  4.624911e+06      0.4995                 NaN   \n",
       "16     T3       1  376600.8723  4.625090e+06      2.1740                 NaN   \n",
       "17     P5       1  376541.5990  4.625084e+06      2.6216                 NaN   \n",
       "18     T2       1  376725.5433  4.625038e+06     -0.5633                 NaN   \n",
       "19     T6       1  376423.7044  4.625243e+06      1.7465                 NaN   \n",
       "20     T5       1  376581.2840  4.625168e+06     -0.2327                 NaN   \n",
       "21    T12       1  376918.4131  4.624828e+06      2.8079                 NaN   \n",
       "22     P6       1  376687.0821  4.624924e+06      2.6679                 NaN   \n",
       "23     P1       1  376822.8060  4.624907e+06      5.3093                 NaN   \n",
       "24     T9       1  376615.5168  4.624785e+06      1.2540                 NaN   \n",
       "\n",
       "    Error_(m)   X_error   Y_error   Z_error          X_est         Y_est  \\\n",
       "0    0.022388  0.001036  0.009889  0.020059  377201.544236  4.624613e+06   \n",
       "1    0.046752 -0.041852 -0.005634  0.020059  377034.261157  4.624693e+06   \n",
       "2    0.071421 -0.066701 -0.017925 -0.018184  377151.559414  4.624637e+06   \n",
       "3    0.040367  0.034440  0.013799 -0.015906  377331.448833  4.624573e+06   \n",
       "4    0.040200  0.010731  0.037140  0.011024  377225.301829  4.624656e+06   \n",
       "5    0.012309 -0.002427 -0.008653 -0.008410  376941.323773  4.624769e+06   \n",
       "6    0.019616 -0.019105  0.004137 -0.001640  376539.520499  4.624997e+06   \n",
       "7    0.016997  0.006149 -0.014622 -0.006106  377075.856648  4.624755e+06   \n",
       "8    0.048220 -0.020931  0.016746  0.040082  376537.600173  4.625135e+06   \n",
       "9    0.024304  0.010554 -0.020032 -0.008833  376507.845052  4.625230e+06   \n",
       "10   0.023834  0.021347 -0.010581  0.000632  377002.443643  4.624840e+06   \n",
       "11   0.050853  0.001640 -0.049194 -0.012776  376822.586740  4.624961e+06   \n",
       "12   0.056585  0.015368  0.006360  0.054086  376701.538765  4.624987e+06   \n",
       "13   0.010903 -0.005146 -0.007873 -0.005514  376574.817356  4.625128e+06   \n",
       "14   0.018534  0.009101  0.013263  0.009207  376418.467399  4.625330e+06   \n",
       "15   0.036790  0.036330  0.005592 -0.001533  376891.280422  4.624911e+06   \n",
       "16   0.018039  0.016370 -0.002929 -0.006991  376600.888666  4.625090e+06   \n",
       "17   0.034919 -0.015205  0.019061 -0.024996  376541.583798  4.625084e+06   \n",
       "18   0.053930 -0.021455 -0.034266 -0.035692  376725.521850  4.625038e+06   \n",
       "19   0.019515  0.000151  0.018572 -0.005989  376423.704551  4.625243e+06   \n",
       "20   0.023835 -0.013324 -0.019756  0.000530  376581.270679  4.625168e+06   \n",
       "21   0.015712  0.006892  0.014087 -0.000960  376918.419991  4.624828e+06   \n",
       "22   0.015063 -0.001650  0.004894 -0.014150  376687.080450  4.624924e+06   \n",
       "23   0.053947  0.038573  0.016811  0.033761  376822.844565  4.624907e+06   \n",
       "24   0.000000 -0.000000  0.000000 -0.000000  376615.516800  4.624785e+06   \n",
       "\n",
       "       Z_est  \n",
       "0   1.695859  \n",
       "1   1.782059  \n",
       "2   1.141816  \n",
       "3  -0.500406  \n",
       "4   2.515124  \n",
       "5   1.055990  \n",
       "6   1.447160  \n",
       "7   2.388794  \n",
       "8   5.312682  \n",
       "9  -0.264633  \n",
       "10  1.142332  \n",
       "11  0.449424  \n",
       "12  5.793486  \n",
       "13  1.311086  \n",
       "14 -0.291393  \n",
       "15  0.497967  \n",
       "16  2.167009  \n",
       "17  2.596604  \n",
       "18 -0.598992  \n",
       "19  1.740511  \n",
       "20 -0.232170  \n",
       "21  2.806940  \n",
       "22  2.653750  \n",
       "23  5.343061  \n",
       "24  1.254000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate GCP distribution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import ConvexHull, Delaunay\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "\n",
    "dn = \".\\\\\"\n",
    "fn = \"2017-04-28_GCPs.txt\"\n",
    "print(dn+fn)\n",
    "# read in the file (in the format exported from Photoscan)\n",
    "df=pd.read_csv(dn+fn,delimiter=',',skiprows=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Distance to closest neighbor\n",
    "xyz = df.as_matrix(columns=df.columns[2:5])\n",
    "n = len(xyz)\n",
    "dist = np.zeros((n,1))\n",
    "for i in np.arange(len(xyz)):\n",
    "    # after sorting, index[1] is distance to closest neighbor\n",
    "    dist[i]=np.sort(np.sqrt(np.sum((xyz-xyz[i,:])**2.,1)))[1] \n",
    "df['dist'] = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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58+fj5+fHnDlzaNSokZMjVko5g16ZOEHhwoUZNGgQv//+O8uXL3d2OLeV2gZcYLvK+uqr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\nufkfA5PLk+0dpdgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c8a8fa828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine 2D convex hull\n",
    "xy = df.as_matrix(columns=df.columns[2:4])\n",
    "hull = ConvexHull(xy)\n",
    "plt.plot(xy[:,0], xy[:,1], 'o')\n",
    "for simplex in hull.simplices:\n",
    "    plt.plot(xy[simplex, 0], xy[simplex, 1], 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24c8b3bc828>,\n",
       " <matplotlib.lines.Line2D at 0x24c8b3bc9e8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LKdUReF5r3VMpNR7Yp7Webo2Ha61bKaVqACuBh7XWaxzO9QXctNbnrffLkZjL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\nyo0TE4fD4XCUm/8HCW4bUIDOqRsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c8b312908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Is there some metric related to the Delaunay triangulation that might help in the design of GCP layout?\n",
    "tri = Delaunay(xy)\n",
    "plt.plot(xy[:,0], xy[:,1], 'o')\n",
    "plt.triplot(xy[:,0], xy[:,1], tri.simplices.copy())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "376400.0 377400.0 4624500.0 4625400.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x24c8c9246a0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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csIpk+egHB+t8OK6hC4AVwLT0/Zkkjx77uXu9SaR4HDgiXcs6H1gIzG3qZ567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\nNdBc3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c8c6b2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What about a map or distance to the third closest point? (Since three points are required to define a plane)\n",
    "# Is there any relationship between this map and the accuracy of the SfM maps?\n",
    "xmin= 100.*np.floor(np.min(xy[:,0]/100.))\n",
    "xmax= 100.*np.ceil(np.max(xy[:,0]/100.))\n",
    "ymin= 100.*np.floor(np.min(xy[:,1]/100.))\n",
    "ymax= 100.*np.ceil(np.max(xy[:,1]/100.))\n",
    "print(xmin,xmax,ymin,ymax)\n",
    "X,Y = np.meshgrid(np.linspace(xmin,xmax,51),np.linspace(ymin,ymax,51))\n",
    "# print(X,Y)\n",
    "\n",
    "dist3 = np.zeros_like(X)\n",
    "ni,nj = np.shape(X)\n",
    "nk = len(xyz)\n",
    "dist2 = np.zeros(nk)\n",
    "for i in range(ni):\n",
    "    for j in range(nj):\n",
    "       for k in range(nk):\n",
    "          #print(xyz[k,0:2])\n",
    "          dist2[k]= np.sqrt( np.sum((np.array([X[i,j],Y[i,j]])-xyz[k,0:2])**2, 0 ))\n",
    "       # after sorting, index[1] is distance to closest neighbor\n",
    "       dist3[i,j]=np.sort(dist2)[3] \n",
    "\n",
    "cm = plt.cm.get_cmap('coolwarm')\n",
    "plt.pcolormesh(X,Y,dist3,cmap=cm)\n",
    "plt.plot(xy[:,0], xy[:,1], 'ok')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BtjZEFi1C9Y9/jJnXXQf+o49QcMwxCHz2GdCnD5TKSgQ//xxsdTXsV10F9quv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3LtRA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\nxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206e77b78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine 3D convex hull\n",
    "xy = df.as_matrix(columns=df.columns[2:4])\n",
    "hull3 = ConvexHull(xyz)\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter( xyz[:,0], xyz[:,1], xyz[:,2], marker='o')\n",
    "for s in hull3.simplices:\n",
    "    s = np.append(s, s[0])  # Here we cycle back to the first coordinate\n",
    "    ax.plot(xyz[s, 0], xyz[s, 1], xyz[s, 2], \"r-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elevation distribution:\n",
      " count    25.000000\n",
      "mean      1.727452\n",
      "std       1.735774\n",
      "min      -0.563300\n",
      "25%       0.499500\n",
      "50%       1.448800\n",
      "75%       2.504100\n",
      "max       5.739400\n",
      "Name: Z/Altitude, dtype: float64 \n",
      "\n",
      "Distance from nearest GCP:\n",
      " count     25.000000\n",
      "mean      68.588061\n",
      "std       28.015411\n",
      "min       37.924662\n",
      "25%       50.776621\n",
      "50%       63.011069\n",
      "75%       84.925154\n",
      "max      155.696709\n",
      "Name: dist, dtype: float64 \n",
      "\n",
      "Area of 2D hull:  2516.095207032294 m^2  (=  0.25160952070322945  hectares, = 0.6214755161369766  acres.\n",
      "Average 2D hull area per GCP:  100.64380828129177 m^2  (=  0.010064380828129178  hectares, = 0.024859020645479065  acres.\n",
      "Volume of 3D hull:  540286.921419403 m^3\n",
      "Average 3D hull volume per GCP:  21611.47685677612 m^3\n"
     ]
    }
   ],
   "source": [
    "print(\"Elevation distribution:\\n\",df['Z/Altitude'].describe(),'\\n')\n",
    "print(\"Distance from nearest GCP:\\n\",df['dist'].describe(),'\\n')\n",
    "print(\"Area of 2D hull: \",hull.area, 'm^2',' (= ',1e-4*hull.area,' hectares, =',2.47e-4*hull.area,' acres.')\n",
    "print(\"Average 2D hull area per GCP: \",hull.area/n, 'm^2',' (= ',1e-4*hull.area/n,' hectares, =',2.47e-4*hull.area/n,' acres.')\n",
    "print(\"Volume of 3D hull: \",hull3.volume, 'm^3')    \n",
    "print(\"Average 3D hull volume per GCP: \",hull3.volume/n, 'm^3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}